Appraising the Impact of Naraj Barrage on Sedimentation of Chilika Lagoon; the Soft Computing Model for Prediction
Siba Prasad Mishra, Ananta Charan Ojha
Archives of Current Research International · pp. 31–41 · Published 22 Sep 2020
10.9734/acri/2020/v20i630200Abstract
Estimation of suspended sediment transport in a catchment area is very important to manage water resources, construction of dam and barrage, as well as to protect the surrounding environment. The daily monsoon sediment and flow were observed physically and quantity of total sediment input by the two major rivers of the south Mahanadi deltaic rivers to Lagoon Chilika were calculated during pre Naraj barrage (FY 2000 to 2003) and post Naraj Barrage period (FY’s 2004, 2012, 2013) establishing an observatory in the rivers the Daya and the Bhargovi.[b] The non-linear complex relationship between quantity of suspended sediment transport and volume of river-discharge inflicts challenge to the estimation process. In this paper, two southern-most distributaries, the Daya and the Bhargovi of the Mahanadi River System which flow into Chilika lagoon are studied. Random Forest, an ensemble machine learning algorithm is used to estimate the transport of sediment by these two distributaries using predictive modeling. Predicted figures based on the gathered data from these distributaries during pre-barrage period 2000-2003 have been compared with the observed data gathered in post-barrage years 2004, 2012 and 2013. Comparative data suggests that the construction of Naraj barrage has significantly reduced the concentration of sediment influx into Chilika lagoon while controlling the discharge through effective barrage management.
Cited by 1
Srinivas Kolluru, Shirishkumar S. Gedam, Shard Chander · Geocarto International · 2023
Related research
- Anthropocene Physiography and Morphology of Chilika; India — shares topic coverage
- Prediction by Soft Computing, Planning, and Strategy Building of Aquatic Catch: Chilika Lagoon, Odisha, India — shares topic coverage
- Assessment of the Different Machine Learning Models for Prediction of Cluster Bean (Cyamopsis tetragonoloba L. Taub.) Yield — shares topic coverage
Article metrics
Real usage data collected on this platform.
0
Page views
0
PDF downloads
0
Outbound clicks
1
Citations
Views by country
Approximate, from request IP at view time — not citizenship or institution. Countries with fewer than 5 views are grouped as "Other".
No views recorded yet.
Traffic sources
Referring site, by host.
No traffic recorded yet.
Views and downloads exclude known bots/crawlers. Citations combines this platform's own DOI-resolved index with each external source's own reported total — see Cited by above for individually listed citing works. Last refreshed 0 seconds ago.